Using high resolution satellite imagery to map aquatic macropyhtes on multiple lakes in northern Indiana

• Main Author: Gidley, Susan
• Other Authors: Johnson, Daniel P.
• Language: en_US
• Published: 2009
• Subjects: aquatic plants; aquatic macropyhtes; high resolution; satellite imagery; remote sensing; Quickbird; freshwater; lakes; Indiana; Lagrange; Noble; Freshwater plants > Indiana; Lakes > Indiana; Remote sensing > Indiana; LaGrange County (Ind.); Noble County (Ind.)
• Online Access: http://hdl.handle.net/1805/2027

Resource managers need to be able to quickly and accurately map aquatic plants in freshwater lakes and ponds for regulatory purposes, to monitor the health of native species and to monitor the spread of invasive species. Site surveys and transects are expensive and time consuming, and low resolution imagery is not detailed enough to map multiple, small lakes spread out over large areas. This study evaluated methods for mapping aquatic plants using high resolution Quickbird satellite imagery obtained in 2007 and 2008. The study area included nine lakes in northern Indiana chosen because they are used for recreation, have residential development along their shorelines, support a diverse wildlife population, and are susceptible to invasive species. An unsupervised classification was used to develop two levels of classification. The Level I classification divided the vegetation into detailed classes of emergent and submerged vegetation based on plant structure. In the Level II classification, these classes were combined into more general categories. Overall accuracy of the Level I classification was 68% for the 2007 imagery and 58% for the 2008 imagery. The overall accuracy of the Level II classification was higher for both the 2007 and 2008 imagery at 75% and 74%, respectively. Classes containing bulrushes were the least accurately mapped in the Level I classification. In the Level II classification, the least accurately mapped class was submerged vegetation. Water and man-made surfaces were mapped with the highest degree of accuracy in both classification schemes. Overhanging trees and shore vegetation contributed to classification error. Overall, results of this research suggest that high resolution imagery provides useful information for natural resource managers. It is most applicable to mapping general aquatic vegetation categories, such as submerged and emergent vegetation, and providing general estimates of plant coverage in lakes. Better methods for mapping individual species, species assemblages, and submerged vegetation constitute areas for further research. === Indiana University-Purdue University Indianapolis (IUPUI)